Fun Generators
Server Details
Connect Claude, GPT, and any MCP-compatible AI agent directly to FunGenerators.com. Generate names, insults, job titles, lorem ipsum, lottery numbers, and much more — all from a single tool call.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.1/5 across 2 of 2 tools scored.
The two tools have completely distinct purposes: one lists available generators, and the other invokes a specific generator. There is no overlap or ambiguity between them, making it clear when to use each tool.
The tools follow a consistent verb_noun pattern (list_all_generation_tools, generate), but the naming is slightly inconsistent as 'generate' is a verb alone while the other includes a noun. However, both are clear and readable, with only minor deviation from a strict pattern.
With only 2 tools, the server feels thin for a 'Fun Generators' domain that implies multiple types of generators. This minimal set may limit agent capabilities, as it lacks tools for managing or customizing generators beyond listing and invoking them.
The tools cover basic listing and invocation, but there are notable gaps for a generative domain, such as creating, updating, or deleting generators, or handling user preferences. Agents can work around this by using the provided tools, but the surface is incomplete for full lifecycle management.
Available Tools
2 toolsgenerateGenerate ToolAInspect
Invoke a specific FunGenerators generator. Provide the type and slug from list_all_generation_tools, plus any optional parameters the generator accepts (e.g. sex, place, category for name generators).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The specific generator slug. Use list_all_generation_tools to find valid slugs for each type. | |
| type | Yes | The generator type. Examples: name, text, insult, job-title, numbers, lorem-ipsum, lottery. | |
| options | No | Optional generator-specific parameters as key-value pairs. For name generators: sex (male|female), place (country name), category. For lorem-ipsum: texttype (paragraphs|sentences|words). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so the description carries full behavioral disclosure burden. It identifies the external service (FunGenerators) and mentions optional parameters, but fails to disclose side effects, idempotency, rate limits, or what the generator invocation returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, dense sentence that front-loads the primary action ('Invoke') and efficiently communicates the prerequisite workflow and parameter structure with zero extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description adequately covers the input parameter workflow but leaves significant gaps regarding return values, error handling, and behavioral characteristics of the generator invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description reinforces the dependency on list_all_generation_tools for valid values, but the examples provided (sex, place, category) are already documented in the schema's options property description, adding minimal semantic value beyond the structured data.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'Invoke[s] a specific FunGenerators generator,' providing a specific verb and resource. It effectively distinguishes from sibling tool list_all_generation_tools by stating that tool provides the required type and slug parameters, implying the discovery vs. execution workflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear workflow guidance by instructing users to obtain type and slug from list_all_generation_tools. However, it lacks explicit 'when not to use' guidance or mention of error states (e.g., invalid slugs).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_all_generation_toolsList All Generation ToolsAInspect
List all available FunGenerators tools. Returns a catalog of generators grouped by type. Use the returned type and slug values to call the generate tool.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number for paginated results. Starts at 1. Each page returns up to 50 generators. | |
| type | No | Filter by generator type. Valid values: name, text, insult, job-title, numbers, lorem-ipsum, lottery. Omit to return all types. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so description carries full burden. It discloses output structure ('catalog of generators grouped by type') and return values ('type and slug'), but lacks other behavioral details like rate limits, caching, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: purpose statement, output format, and usage guidance. No redundancy or wasted words. Information is front-loaded with the action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but description compensates by explaining return structure (catalog grouped by type) and specific fields (type and slug). Explains relationship to sibling tool. Minor gap: could explicitly mention pagination behavior, though schema covers the page parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline score applies. The description does not add parameter-specific semantics beyond the schema, but the schema fully documents both page and type parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States specific action (List) and resource (FunGenerators tools), distinguishes from sibling 'generate' by clarifying this returns a catalog for discovery rather than performing generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly defines the workflow: 'Use the returned type and slug values to call the generate tool.' This clearly indicates when to use this tool (before generate, for discovery) and how it relates to the sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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